alpine

alpine estimates transcript abundance from RNA-seq data using bias-corrected models that account for fragment sequence features such as GC content.

Key Features:

• Fragment Sequence Bias Modeling: Models sample-specific biases associated with fragment sequence features, including GC content, that affect RNA-seq quantification.
• Bias-Corrected Transcript Quantification: Incorporates fragment sequence features into abundance estimation to correct systematic biases in transcript isoform identification.
• Reduction of False Positives: Improves differential expression analysis by reducing false-positive gene expression changes compared with methods such as Cufflinks.
• Bias Exploration Visualization: Provides analytical visualization methods for examining bias patterns in RNA-seq datasets.

Scientific Applications:

• Differential Gene Expression Analysis: Produces bias-corrected transcript abundance estimates for improved detection of gene expression changes.
• Transcript Isoform Quantification: Enhances identification and quantification of transcript isoforms from RNA-seq data.
• Functional Genomics Studies: Supports accurate interpretation of RNA-seq experiments in transcriptomics research.

Methodology:

alpine models fragment sequence features such as GC content to estimate sample-specific biases in RNA-seq data and incorporates these features into transcript abundance estimation algorithms to produce bias-corrected quantification.